Introduction

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Very useful

You can access the course materials quickly from

https://laurencefrank.github.io/R

Overview of this course

Program

Goal of this course

1. Learn to programme in R

and build the foundation for a successful scripting career.

2. Learn statistical programming

What is statistical programming?

Broadly speaking, computer programming differs from statistical programming in the following way:

• Computer programming is more focused on software development.

• Statistical programming is more focused on data analysis and the communication of the results.

Source figure: R for Data Science

What is R?

Software

The origin of R

• R is a language and environment for statistical computing and for graphics

• GNU project (100% free software)

• Managed by the R Foundation for Statistical Computing, Vienna, Austria.

• Community-driven

• Based on the object-oriented language S (1975)

Integrated Development Environment

Source figure: R for Data Science

RStudio

• Aggregates all convenient information and procedures into one single place
• Allows you to work in projects
• Manages your code with highlighting
• Gives extra functionality (Shiny, knitr, markdown, LaTeX)
• Allows for integration with version control routines, such as Git.

How does R work?

Objects and elements

• R works with objects that consist of elements. The smallest elements are numbers and characters.

  • These elements are assigned to objects.
  • A set of objects can be used to perform calculations
  • Calculations can be presented as functions
  • Functions are used to perform calculations and return new objects, containing calculated (or estimated) elements.

The help

• Everything that is published on the Comprehensive R Archive Network (CRAN) and is aimed at R users, must be accompanied by a help file.

• If you know the name of the function that performs an operation, e.g. anova(), then you just type ?anova or help(anova) in the console.

• If you do not know the name of the function: type ?? followed by your search criterion. For example ??anova returns a list of all help pages that contain the word ‘anova’

• Alternatively, the internet will tell you almost everything you’d like to know and sites such as http://www.stackoverflow.com and http://www.stackexchange.com, as well as Google can be of tremendous help.

  • If you google R related issues; use ‘R:’ as a prefix in your search term

Assigning elements to objects

• Assigning things in R is very straightforward:

  • you just use <-

• For example, if you assign the value 100 (an element) to object a, you would type

a <- 100

When reading that code say: “a gets value 100” in your head.

Good coding practice:

• <- is a pain to type, but don’t use = instead, it will work, but it will cause confusion later.

• Use RStudio’s keyboard shortcut: Alt (or Option) + - (the minus sign).

• Notice that RStudio automatically surrounds <- with spaces (good code formatting practice).

Calling objects

• Calling things in R is also very straightforward:

  • you just use type the name you have given to the object

• For example, we assigned the value 100 to object a. To call object a, we would type

a

## [1] 100

Writing code

This is why we use R-Studio.

Organise your work in RStudio

Use RStudio Projects

Every time you start a new data analysis project, create a new RStudio Project.

Because you want your project to work:

• not only now, but also in a few years;
• when the folder and file paths have changed;
• when collaborators want to run the code on their computer.

RStudio Projects create a convention that guarantees that the project can be moved around on your computer or onto other computers and will still “just work”.

Example structure for data analysis project

Every time you want to work on this project: open the project by clicking the .Rproj file.

Practical A